Whisper App README (#57)

* Added README for Whisper App

* Add video to README.md

Author: rohansjoshi

whisper/android/WhisperApp/README.md

@@ -0,0 +1,25 @@
+# Whisper Demo App
+
+This app runs the Whisper model with the Qualcomm backend in ExecuTorch. The model itself runs on the Qualcomm HTP (Hexagon Tensor Processor), an AI accelerator in Qualcomm SoCs. You need an Android smartphone with a [supported Qualcomm chipset](https://github.com/pytorch/executorch/tree/main/backends/qualcomm).
+
+### Build the ExecuTorch Android library
+
+Checkout [Whisper JNI Bindings](https://github.com/pytorch/executorch/pull/13525) (if it is not merged already). Build the [ExecuTorch Android library with QNN backend](https://github.com/pytorch/executorch/blob/main/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md).
+
+### Export the audio processing and model .pte files
+
+There are two steps, audio processing and the Whisper model (encoder+decoder), which are both done via ExecuTorch.
+
+1) Run the script `extension/audio/mel_spectrogram.py` to export `whisper_preprocess.pte`
+2) Run the scrint `examples/qualcomm/oss_scripts/whisper/whisper.py` to export `whisper_qnn_16a8w.pte`
+
+Move these two .pte files along with `tokenizer.json` to `/data/local/tmp/whisper` on device.
+
+### Run app
+
+Open WhisperApp in Android studio. Copy the Android library `executorch.aar` which should have audio JNI bindings and the Qualcomm HTP libraries, into `app/libs`.
+
+
+
+https://github.com/user-attachments/assets/ff8c71c5-b734-4ed4-8382-70a429830665
+

whisper/android/WhisperApp/app/src/main/java/com/example/whisperapp/MainActivity.kt

@@ -148,7 +148,7 @@ class MainActivity : ComponentActivity(),  WhisperCallback {
         val byteArray = arrayOf(byteBuffer.array())
 
         val whisperModule = WhisperModule("/data/local/tmp/whisper/whisper_qnn_16a8w.pte",
-            "/data/local/tmp/rohansjoshi/executorch/whisper/tokenizer.json")
+            "/data/local/tmp/whisper/tokenizer.json")
 
         Log.v(TAG, "Starting transcribe")
         whisperModule.transcribe(128, byteArray, this@MainActivity) // this runs runner.transcribe()